基于三维激光点云的靶标探测系统研究与试验

doi:10.6048/j.issn.1001-4330.2023.11.027

新疆农业科学 ›› 2023, Vol. 60 ›› Issue (11): 2833-2841.DOI: 10.6048/j.issn.1001-4330.2023.11.027

• 农业装备工程与机械化·草业 • 上一篇下一篇

基于三维激光点云的靶标探测系统研究与试验

齐亚聪¹(), 陈毅飞²^,³, 杨会民²^,³, 王学农¹^,²^,³()

1.新疆农业大学机电工程学院,乌鲁木齐 830052
2.农业农村部林果棉装备科学观测实验站,乌鲁木齐 830091
3.新疆农业科学院农业机械化研究所,乌鲁木齐 830091

收稿日期:2023-02-19 出版日期:2023-11-20 发布日期:2023-12-07
通信作者: 王学农(1964-),男,陕西汉中人,研究员,硕士生导师,研究方向为农业机械化技术装备,(E-mail)xjwxn2010@sina.com
作者简介:齐亚聪(1996-),男,河南兰考人,硕士研究生,研究方向为农业智能化装备, (E-mail)1477605991@qq.com
基金资助:
新疆设施农业智能管控技术重点实验室(XJYS1703);新疆农业科学院青年科技骨干创新能力培养项目“基于2D与3D数据多模态融合的植株冠层靶标识别与分割方法研究”(xjnkg-2023026);教育部第二批新工科研究与实践项目“新工科教育体系下农业工程类专业人才培养质量提升路径探索与实践”(E-SPNL20202326,地方高校组)

Research and test of target detection system based on 3D laser point cloud

QI Yacong¹(), CHEN Yifei²^,³, YANG Huimin²^,³, WANG Xuenong¹^,²^,³()

1. College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052,China
2. Scientific Observation and Experimental Station of Forest Fruit, Cotton and Facility, Agriculture Equipment, Ministry of Agriculture and Rural Affairs, Urumqi 830091, China
3. Agricultural Mechanization Institute, Xinjiang Academy of Agricultural Sciences, Urumqi 830091,China

Received:2023-02-19 Online:2023-11-20 Published:2023-12-07
Correspondence author: WANG Xuenong (1964-), male, Shannxi Province, researcher, master tutor, research direction: agricultural mechanization technology and equipment, (E-mail)xjwxn2010@sina.com
Supported by:
Xinjiang Key Laboratory of Facilities Agriculture Intelligent Control Technology(XJYS1703);Xinjiang Academy of Agricultural Sciences Young Science and Technology Backbone Innovation Ability Training Project."Research on the Method of Plant Crown Target Identification and Segmentation Based on the Multimodal Fusion of 2D and 3D Data"(xjnkg-2023026);The Second Batch of New Engineering Research and Practice Project of the Ministry of Education,"Exploration and Practice of the Path of Improving the Quality of the Training of Agricultural Engineering Professionals under the New Engineering Education System"(E-SPNL20202326;Local university group)

摘要/Abstract

摘要：

【目的】 使用R-Fans-32三维激光雷达(LiDAR)研究植株三维激光点云与植株叶面积之间的关系,为变量喷雾系统提供数据支撑。【方法】 假设植株激光点云数量与叶面积之间存在线性关系。搭建基于三维激光点云的靶标探测的试验系统,先测量靶标植株的高度来探究该探测系统的精度,激光雷达以10Hz的扫描频率和1m的探测距离实现对10株番茄的三维点云数据的获取,激光雷达上位机软件Ctrlview实现对三维激光点云数据的储存。利用Cloud Compare软件对储存的点云数据进行处理,利用LiDAR360软件对植株进行高度测量和点云数量的获取。对采集的植株点云进行数量统计,利用CL-202植物叶面积测量仪对采摘的靶标植株叶片测量叶面积,验证植株点云与叶面积之间的关系。【结果】 激光雷达探测所得到的番茄植株的高度与手工测量值的最大相对误差为7.92%。利用线性函数拟合植株点云数量与叶面积,拟合度为0.7805,最大相对误差为5.64%。【结论】 设计了一种用于探究基于激光点云的变量喷雾系统可行性的试验系统,依据三维激光点云计算植株的叶面积精度良好,R-Fans-32三维激光雷达可作为变量喷雾系统的探测部件。

关键词: 变量喷雾; 激光雷达; 信息采集; 三维点云; 叶面积; 测量

Abstract:

【Objective】 Variable spray system can reduce the waste of liquid medicine and reduce the pollution of liquid medicine to land and water. The acquisition of plant geometric parameter information is an important prerequisite for implementing variable spray. R-Fans-32 three-dimensional laser radar (LiDAR) was used to explore the relationship between plant 3D laser point cloud and plant leaf area, providing data support for variable spray machine.【Methods】 It was assumed that there was a linear relationship between the number of laser point cloud and leaf area. The target detection based on 3 d laser point cloud of the test system was set to measure the height of the target plant to explore the accuracy of laser radar, laser radar with 10 Hz scanning frequency and 1m of the detection range of 10 strains of tomato three-dimensional point cloud data acquisition of laser radar PC software Ctrlview implementation of 3 d laser point cloud data storage. Cloud Compare software was used to process the stored point cloud data, and LiDAR360 software was used to measure the height of plants and obtain the number of point Cloud. The quantity of collected plant point cloud was counted, and the leaf area of picked target plant leaves was measured by CL-202 plant leaf area meter to verify the relationship between plant point cloud and leaf area.【Results】 The experimental results showed that the maximum relative error between the height of tomato plants detected by lidar and manual measurement was 7.92%. The linear function was used to fit the number of plant point cloud and leaf area, with a fitting degree of 0.7805 and a maximum relative error of 5.64%.【Conclusion】 An experimental system was designed to explore the feasibility of variable spray system based on laser point cloud. The accuracy of plant leaf area prediction based on 3d laser point cloud was good, and 3D LIDAR R-FAN-32 could be used as the component of variable spray system for crop detection.

Key words: variable spray; LiDAR; information collection; three-dimensional point cloud; leaf area; measurement

中图分类号:

齐亚聪, 陈毅飞, 杨会民, 王学农. 基于三维激光点云的靶标探测系统研究与试验[J]. 新疆农业科学, 2023, 60(11): 2833-2841.

QI Yacong, CHEN Yifei, YANG Huimin, WANG Xuenong. Research and test of target detection system based on 3D laser point cloud[J]. Xinjiang Agricultural Sciences, 2023, 60(11): 2833-2841.

图/表 16

图1 R-Fans-32激光雷达实物图

Fig.1 R-Fans-32 Actual LiDAR drawing

表1 R-Fans-32 激光雷达参数

Tab.1 R-Fans-32 LiDAR Parameter Tab.

参数 Parameter	数值 Figure
激光帧频Laser frame frequency(Hz)	5、10、20
最大探测距离Maximum range(m)	200
距离分辨率Range resolution(mm)	4
测距精度Range accuracy(cm)	<3

图2 变量喷雾系统整体结构示意注:1.行走装置;2.喷雾装置;3.探测装置;4.控制装置

Fig.2 Schematic diagram of the overall structure of the variable spray system Note:1.Walking device; 2.Spray device; 3.Detection device; 4.Control device

图3 靶标检测试验系统

Fig.3 Target Detection and Test system

图4 系统结构示意

Fig.4 Schematic diagram of the system structure

图5 番茄植株实物

Fig.5 Physical picture of tomato plants

表2 系统探测植株高度

Tab.2 Height analysis of plant detection

编号 NO.	手工测量值 Manual measure ments (m)	系统测量值 System measure ments (m)	差值 Error value (m)	相对误差 Fractional error (%)
1	0.92	0.89	-0.03	3.26
2	1.06	1.02	-0.04	3.77
3	0.88	0.84	-0.04	4.55
4	0.79	0.78	-0.01	1.27
5	0.90	0.92	+0.02	2.22
6	0.94	0.91	-0.03	3.19
7	0.99	1.00	+0.01	1.01
8	1.01	0.93	-0.08	7.92
9	0.97	0.91	-0.06	6.19
10	0.83	0.82	-0.01	1.20

图6 点云数据获取与处理流程

Fig.6 Flow chart of point cloud data acquisition and processing

图 7 番茄植株点云示意

Fig.7 Schematic diagram of the point cloud in tomato plants

图8 分割后的点云示意

Fig.8 Schematic representation of the point cloud after the segmentation

图9 去噪后的点云示意

Fig.9 Schematic representation of the point cloud after denoising

表3 CL-202植物叶面积仪参数

Tab.3 CL-202 Parameter table of plant leaf area meter

参数 Parameter	数值 Figure
扫描激光Scan the laser(mm)	675
分辨率Resolution ratio(mm²)	0.025
最大测量宽度 Maximum measurement width(mm)	150
最大扫描速度Maximum scan speed(mm/s)	127
精度Accuracy(%)	±1

表4 植物叶面积仪测量与系统探测

Tab.4 Analysis of measuring method and System detection method

编号 NO.	叶面积 Leaf area (cm²)	点云数 Point cloud number	探测叶面积 Probe leaf area (cm²)	相对误差 Fractional error (%)
1	1 504.1	1 491	1 589.0	5.64
2	1 617.9	1 566	1 676.6	3.63
3	1 466.0	1 401	1 483.8	1.21
4	1 506.9	1 421	1 507.2	0.02
5	1 583.6	1 464	1 557.4	1.65
6	1 586.9	1 472	1 566.8	1.27
7	1 398.1	1 296	1 361.1	2.65
8	1 620.0	1 498	1 597.2	1.41
9	1 624.3	1 494	1 592.5	1.96
10	1 481.6	1 381	1 460.4	1.43

图10 CL-202植物叶面积仪

Fig.10 C L-202 Plant Leaf Area Instrument

图11 植株高度探测值与测量值线性模型

Fig.11 Linear model of plant height detection and measured values

图12 叶面积与点云数的线性模型

Fig.12 Linear model of leaf area and point clouds

参考文献 25

[1]	周鸣川. 脉宽调制(PWM)变量喷雾及视觉辅助对靶植保技术研究[D]. 杭州: 浙江大学, 2015.
	ZHOU Mingchuan. Pulse width modulation variable spray and target spray based on computer technology research[D]. Hangzhou: Zhejiang University, 2015.
[2]	何雄奎. 我国植保无人机喷雾系统与施药技术[J]. 农业工程技术, 2018, 38(9):33-38.
	HE Xiongkui. Spraying system and application technology of Plant Protection UAV in China[J]. Agricultural Engineering Technology, 2018, 38(9):33-38.
[3]	刘慧, 夏伟, 沈跃, 等. 基于实时传感器的精密变量喷雾发展概况[J]. 中国农机化学报, 2016, 37(3):238-242.
	LIU Hui, XIA Wei, SHEN Yue, et al. Development overview of precision variable spraying based on real-time sensor technology[J]. Journal of Chinese Agricultural Mechanization, 2016, 37(3):238-244,260.
[4]	Rosell J R, Sanz R. A review of methods and applications of the geometric characterization of tree crops in agricultural activities[J]. Computers and Electronics in Agriculture, 2012, 81:124-141. DOI URL
[5]	张美娜, 吕晓兰, 雷哓晖. 可移植的对靶喷雾控制系统设计与试验[J]. 江苏农业学报, 2017, 33(5):1182-1187.
	ZHANG Meina, LYU Xiaolan, LEI Xiaohui. Design and testing on a transplantable target spraying control system for the spraying machine[J]. Jiangsu Journal of Agricultural Sciences, 2017, 33(5):1182-1187.
[6]	翟长远, 朱瑞祥, 张佐经, 等. 精准施药技术现状分析[J]. 农机化研究, 2010, 32(5): 9-12.
	ZHAI Changyuan, ZHU Ruixiang, ZHANG Zuojing, et al. Status Analysis of Precision Pesticide Application Techniques[J]. Journal of Agricultural Mechanization Research, 2010, 32(5):9-12.
[7]	俞龙, 洪添胜, 赵祚喜, 等. 基于超声波的果树冠层三维重构与体积测量[J]. 农业工程学报, 2010, 26(11):204-208.
	YU Long, HONG Tiansheng, ZHAO Zuoxi, et al. 3D-reconstruction and volume measurement of fruit tree canopy based on ultrasonic sensors[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(11):204-208.
[8]	Hossein M, Saeid M, Barat G, et al. Ultrasonic sensing of pistachio canopy for low-volume precision spraying[J]. Computers and Electronics in Agriculture, 2015, 112:149-160. DOI URL
[9]	Joan R.Rosell, Jordi Llorens, Ricardo Sanz, et al. Obtai-ning the three-dimensional structure of tree orchards from remote 2D terrestrial LIDAR scanning[J]. Agricultural and Forest Meteorology, 2009, 149(9):1505-1515. DOI URL
[10]	谷趁趁, 翟长远, 陈立平, 等. 基于激光雷达的树形靶标冠层叶面积探测模型研究[J]. 农业机械学报, 2021, 52(11):278-286.
	GU Chenchen, ZHAI Changyuan, CHEN Liping, et al. Detection Model of Tree Canopy Leaf Area Based on LiDAR Technology[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(11):278-286.
[11]	Medeiros H, Kim D, Sun J, et al. Modeling Dormant FruitTrees for Agricultural Automation[J]. Journal of Field Robotics, 2017, 34(7):1203-1224. DOI URL
[12]	管贤平, 刘宽, 邱白晶, 等. 基于机载三维激光扫描的大豆冠层几何参数提取[J]. 农业工程学报, 2019, 35(23):96-103.
	GUAN Xianping, LIU Kuan, QIU Baijing, et al. Extraction of geometric parameters of soybean canopy by airborne 3D laser scanning[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(23):96-103.
[13]	吴志鹏, 付威, 娄朝霞, 等. 基于激光雷达的枣树轮廓测量平台的设计与试验[J]. 农机化研究, 2020, 42(12):52-57.
	WU Zhipeng, FU Wei, LOU Zhaoxia, et al. Design and test of a lidar based profile measurement platform for jujube[J]. Journal of Agricultural Mechanization Research, 2020, 42(12):52-57.
[14]	俞龙, 黄健, 赵祚喜, 等. 丘陵山地果树冠层体积激光测量方法与试验[J]. 农业机械学报, 2013, 44(8):224-228.
	YU Long, HUANG Jian, ZHAO Zuoxi, et al. Laser Measurement and Experiment of Hilly Fruit Tree Canopy Volume[J]. Transactions of the Chinese Society for Agricultural Machinery, 2013, 44(8):224-228.
[15]	张美娜, 吕晓兰, 邱威, 等. 基于三维激光点云的靶标叶面积密度计算方法[J]. 农业机械学报, 2017, 48(11):172-178.
	ZHANG Meina, LYU Xiaolan, QIU Wei, et al. Calculation Method of Leaf Area Density Based on Three-dimensional Laser Point Cloud[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(11):172-178.
[16]	刘芳, 冯仲科, 杨立岩, 等. 基于三维激光点云数据的树冠体积估算研究[J]. 农业机械学报, 2016, 47(3):328-334.
	LIU Fang, FENG Zhongke, YANG Liyan, et al. Estimation of Tree Crown Volume Based on 3D Laser Point Cloud Data[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(3):328-334.
[17]	Bailey B N, MacAfee W. Rapid, high-resolution measurement of leaf area and leaf orientation using terrestrial LiDAR scanning data[J]. Measurement Science and Technology, 2017, 28(6).
[18]	刘洋, 刘荣高, 陈镜明, 等. 叶面积指数遥感反演研究进展与展望[J]. 地球信息科学学报, 2013, 15(5):734-743.
	LIU Yang, LIU Ronggao, CHEN Jingming, et al. Current Status and Perspectives of Leaf Area Index Retrieval from Optical Remote Data[J]. Journal of Geo-information Science, 2013, 15(5):735-743. DOI URL
[19]	Abuelgasim A A, Fernandes R A, Leblanc S G. Evaluation of national and global LAI products derived from optical remote sensing instruments over Canada[J]. IEEE Transactions on Geoscience & Remote Sensing, 2006, 44(7):1872-1884.
[20]	Comba L, Biglia A, Aimonino D R, et al. Leaf Area Index evaluation in vineyards using 3D point clouds from UAV imagery[J]. Precision Agriculture, 2020, 21:881-896. DOI
[21]	胡培, 张文爱, 王秀, 等. 基于激光传感器检测树冠大小的实验平台设计[J]. 中国农机化学报, 2015, 36(5):227-230.
	HU Pei, ZHANG Wenai, WANG Xiu, et al. Experimental platform design for detecting tree canopy volume based on laser scanning sensor[J]. Journal of Chinese Agricultural Mechanization, 2015, 36(5):227-230.
[22]	毕松, 王宇豪. 基于自适应半径滤波的农业导航激光点云去噪方法研究[J]. 农业机械学报, 2021, 52(11):234-243.
	BI Song, WANG Yuhao. LiDAR Point Cloud Denoising Method Based on Adaptive Radius Filter[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(11):234-243.
[23]	王明华, 王永军, 闾家阳, 等. 基于激光位移传感器的物体表面形貌测量系统[J]. 宇航计测技术, 2022, 42(2):33-38.
	WANG Minghua, WANG Yongjun, LU Jiayang, et al. Measurement System of Surface Topography Based on Laser Displacement Sensor[J]. Journal of Astronautic Metrology and Measurement, 2022, 42(2):33-38.
[24]	李方一, 黄璜, 官春云. 作物叶面积测量的研究进展[J]. 湖南农业大学学报(自然科学版), 2021, 47(3):274-282.
	LI Fangyi, HUANG Huang, GUAN Chunyun. Review on measurement of crop leaf area[J]. Journal of Hunan Agricultural University (Natural Science Ed), 2021, 47(3):274-282.
[25]	李方一, 李锦卫, 黄璜, 等. 单株油菜叶面积田间无损测量方法和策略研究[J]. 激光生物学报, 2021, 30(6):505-517.
	LI Fangyi, LI Jinwei, HUANG Huang, et al. Research on Non-destructive Measurement Methods and Strategies of Individual Rape Leaf Area in Field[J]. Acta Laser Biology Sinica, 2021, 30(6):505-517.

基于三维激光点云的靶标探测系统研究与试验

Research and test of target detection system based on 3D laser point cloud

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[14]	牛莹莹;廖康;赵世荣;庞洪翔;徐桂香;江振斌;牛真真. 不同栽植密度库尔勒香梨冠层光合有效辐射变化规律研究[J]. , 2016, 53(3): 420-428.
[15]	刘辉;郑逢令;安沙舟;李超;热孜宛姑丽·吐尔孙阿吉;阿斯娅·曼力克. 天山北坡4种典型草原植物叶干重与叶面积的关系[J]. , 2016, 53(12): 2321-2327.